Add VVIX regime filter for backtesting options
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from .backtest_filter import BacktestFilter
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from .backtest_filter import BacktestFilter
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from .volatility_regime_filter import VolatilityRegimeFilter
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from .volatility_regime_filter import VolatilityRegimeFilter
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from .vvix_regime_filter import VVIXRegimeFilter
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backtesting/filter/vvix_regime_filter.py
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backtesting/filter/vvix_regime_filter.py
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from datetime import datetime, timedelta
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from dotenv import load_dotenv
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from os import getenv
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from pandas import concat, DataFrame, Series
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from database.ohlc import ohlc
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from .backtest_filter import BacktestFilter
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load_dotenv()
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class VVIXRegimeFilter(BacktestFilter):
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def __init__(self):
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self.backtest_filter = self.filter()
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def filter(self) -> DataFrame:
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data_start_date = datetime.strptime(getenv('OPTION_DATA_START_DATE'), '%Y-%m-%d')
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now = datetime.now()
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vvix_data = ohlc(
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symbol = 'VVIX.XO',
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timeframe = '1d',
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start_date = data_start_date - timedelta(weeks = 52),
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end_date = now
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)
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vvix_data.rename(columns = {'Timestamp': 'Date'}, inplace = True)
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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Filtering is based on the previous day's close, so the current date can be included even though the
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data may not be availble yet.
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This allows for utilizing the filter in live trading to decide whether to trade prior to market open.
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"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
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vvix_data = concat([
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vvix_data,
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DataFrame({
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'Date': [datetime.combine(now, datetime.min.time())],
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'Open': [0.0],
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'High': [0.0],
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'Low': [0.0],
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'Close': [0.0],
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'Volume': [0.0]
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})],
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ignore_index = True
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)
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percent_rank = lambda x: Series(x).rank(pct = True).iloc[-1]
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vvix_data['Percent Rank'] = vvix_data['Close'].shift(1).rolling(window = 252).apply(percent_rank)
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filtered_data = vvix_data[vvix_data['Date'] >= data_start_date].copy()
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filtered_data['Trade Allowed'] = filtered_data['Percent Rank'] < 0.50
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filtered_data = filtered_data[['Date', 'Trade Allowed']].reset_index(drop = True)
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return filtered_data
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